BEHIND THE DATA

Project Motivation

I first began this project when I realized as studying and other commitments in my life took me away my time, the actual time I got to go on a dedicated motorbike ride (or a “joy-ride”) for the sake of enjoyment became sparing. This data spans all the way back from project 2 and I remember sitting down one evening and thinking up of ideas for project 2, I really decided to make this one personal and beneficial to me… So for this when time allowed me to go on a “joy-ride” I simply wanted to find out the best way to maximize these moments so that they didn’t go to waste

So for this I centered the research around what I had assumed certain parameters and variables may have an effect on my satisfaction during a ride. Since I still commute with my motorbike as it is my main mode of transport whilst in Auckland. So on every “commute” ride I would observe factors that affected my personal enjoyment.

Data Collection

So how this data was collected? … Let’s Uncover that!First I had employed my first employee tasked to handled to front the collection of data from the respondents, their name being? Google Forms and when I say respondents I actually mean respondent as it was only just me. Anyways I had used a Google Form to collect all the observations for every ride. This approach allowed for a seamless application in collecting the data and then subsequently transferring this into a Google Sheet

For the Google Sheet - rows and columns were organised where each row was a singular response. From this I had the document organised into a CSV to import into R Studio. From here all data manipulation was handled in the R suite. Here analyzing the data and organizing them into visual components and representation was done to deduce and clearly see what actually affected Satisfaction during a motorbike ride for myself.

Variables Explored

The main areas we wanted to explore are listed bellow:

SATISFACTION UNCOVERED

Satisfaction Revealed

Overall independently the data uncovered that when the sun was shining typically satisfaction also peaked. This can clearly be explained that when its sunny the road is dry, the temperature is warm it makes for a more pleasant riding experience especially on a vehicle where traction is only centered around one wheel (rear wheel).

What was a clear distinction in revealing the factors to ride satisfaction was when there was no Road Hazards present. This was pretty self explanatory encountering hazards essentially interruptions in a ride, drive or any travel on the road… Bellow this is expressed more

CHARTING THE JOURNEY

Another Day in Traffic

I have decided to see if the day in the week has an effect on Traffic flow and then subsequently the average satisfaction score. Comparing the day in terms of the types of trafic flow found can give insight on what kind of traffic we can expect on those days as we know traffic flow when it is light improves satisfaction of the ride

From the data we can see that there is a consistent data to show that regardless of day we can see that when traffic is light, the satisfaction of the ride significantly improves, whereas heavy congestion decreases satisfaction. Its safe to say that that Satisfaction and Traffic flow are inversely proportional. When comparing the days we ended up not getting any data points on Saturday or Sunday as time during the data collection restricted us on finding a necessity to ride on those days. Overall we can see that we didn’t come across on Heavy traffic on Friday and overall the Satisfaction of riding on that day was more enjoyable. This could be due to that or maybe construed with Friday being the end of week and wanting to get into the weekend.. ha ha.

Lights, Flow and Satisfaction

My observations conducted also included the amount of traffic lights I had ended up encountering I wanted to see if there was an effect on Satisfaction if I had encountered more traffic lights but also to throw in traffic flow into the comparison to understand what kind of route parameters would affect my Satisfaction In this data I have applied a linear model to show the trends of Satisfaction at different rates of traffic lights in the journey but also between traffic flow. When Traffic flow is Light we can see that regardless of the amount of traffic lights encountered we can see that Satisfaction still remains high or even slightly increases when there is more traffic lights. When it comes to moderate traffic flow and the beginning of some amounts of congestion, as the amount of traffic lights in the journey increase Satisfaction seems to drop whilst when in Heavy congestion the more traffic lights there are Satisfaction surprisingly increases. From analysis this could be explained due to the nature that when there is heavy traffic a motorbike is allowed to split between traffic allowing myself to get to where I need to go quicker. The more traffic lights means that cars are more likely to be stopped and the process of lane splitting becomes a lot more easier and safer when this occurs. In terms of moderate congestion its sort of a weird middle ground, traffic is somewhat flowing making lane splitting not as safe and applicable and therefore more dangerous. Since traffic may not be entirely stopped this could also end up with more time being lost in the journey hence when traffic lights increased during moderate congestion we observe a drop in satisfaction. Finally to simply put it light to no congestion allows me to be faster and I know being faster is directly proportional to Satisfaction :)

Hazards, the Weather and How I Felt

Hazards and Weather both so random but can heavily change the mood of the ride. The list of hazards to track may be random but in certain weather conditions this visual may be an aid to see which hazard ends up being more hazardous in different weather.

So what we can observe immediately in this plot Rain clearly brings down ride satisfaction SIGNIFICANTLY. Overall we can observe there is a consistency that regardless of any weather condition, erratic pedestrians jumping in the way seemed to cause the most drop in satisfaction… as I recall those instances were mainly centered around pedestrians crossing th road around blind corners or when they weren’t really meant to and it’s fair to say that a life or death situation nearly unfolding is not far fetched in dropping ride satisfaction. What came as a shock is that what I though slippery roads would be a major determinant in decreasing ride satisfaction isn’t really the case in this graph. Instead potholes seem to cause the most harm to Satisfaction. Now looking at this in hindsight especially on a wet day hitting a pot hole causes a brief moment of loss of traction (scary experience essentially) - would bring down Satisfaction

Breaking it down into its simple components, the next plot shows Average Satisfaction for each weather condition and also average satisfaction against each hazard:

Satisfaction vs Weather

Satisfaction vs Hazards

Here as expected we can see expected that rainy weather significantly brings down satisfaction. On the contrary we can actually observe that in all instances of slippery roads hazard occuring it in fact is the biggest contributor to decreasing rider satisfaction

Good Flow = Feeling Good?

This graph simply is of personal interest when traffic flow is changing does the amount of hazard change, I’ve also layered it on top of Satisfaction just to see the proportionality towards the average amount of hazards and also the satisfaction as at the end of the day the goal is to find what makes the ride the most satisfying As we know light traffic improves satisfaction, but we can also see that the more congestion the more hazards we observe. This could be of two things. One we are stopped more often and have more time to observe the surroundings more deeply or alternatively the more people on the road simply put it the more hazards in a way.

A VALID SPREAD?

We can see clear trends in our data but compared to the data entries where Satisfaction is at its height and when it is lower, we have a lot more data to clearly show what makes a ride good but nearly not as much to show for what makes a ride bad. This isn’t a flaw in the observations just to simply put it in the time span of the observations being made there were just not as many bad rides. So to improve and understand fully what makes satisfaction decrease in a ride we should run this investigation longer.

WRAPPING THIS UP: FINAL INSIGHTS

Across all my analysis collected from my daily commutes, we can see consistent trends in the data that would affect my riding satisfaction. Unsurprising to us, sunny weather was a consistent factor to bringing satisfaction to a high, this likely being to much more ideal road conditions (safer) thus allowing for more spirited riding with less risk and consequence. Rainy conditions on the other hand was a major contributor to dissatisfaction, especially when paired with more hazards it amplifies the drop in rider satisfaction

Traffic flow also seemed to have its own independent effect on satisfaction. Lighter traffic let to more satisfaction, while congestion tended to make satisfaction drop. To my surprise, more traffic lights did not equate to a worse experience. Instead in heavy traffic, traffic lights seemed to improve the ride by making lane splitting more safe and feasible.

When it came to hazards, slippery roads initially seemed to be not the major contributor to dissatisfaction, again to my surprise initially potholes and pedestrians seemed to be more of an issue towards ride enjoyment. We did also observer a relationship between more traffic and hazards, it had seemed that often than not the more traffic equated to more hazards, this could be explained to more time spent in traffic to observe hazards otherwise.

Lastly, the density plot was used to showed that there was more data entries for positive rides was much larger than the amounts of negative rides. Therefore to safely deduce what makes a ride positive but not so definitive to deduce what makes a ride negative. In short more observations over a longer period would be successful in achieving this